506 citations · 1.3k across the 52 of their papers we have counts for
52 papers
CCD-3DR: Consistent Conditioning in Diffusion for Single-Image 3D Reconstruction
Yan Di, Chenyangguang Zhang, Pengyuan Wang +6
In this paper, we present a novel shape reconstruction method leveraging diffusion model to generate 3D sparse point cloud for the object captured in a single RGB image. Recent met…
DDF-HO: Hand-Held Object Reconstruction via Conditional Directed Distance Field
Chenyangguang Zhang, Yan Di, Ruida Zhang +4
Reconstructing hand-held objects from a single RGB image is an important and challenging problem. Existing works utilizing Signed Distance Fields (SDF) reveal limitations in compre…
Guided Depth Map Super-resolution: A Survey
Zhiwei Zhong, Xianming Liu, Junjun Jiang +2
Guided depth map super-resolution (GDSR), which aims to reconstruct a high-resolution (HR) depth map from a low-resolution (LR) observation with the help of a paired HR color image…
SST: Real-time End-to-end Monocular 3D Reconstruction via Sparse Spatial-Temporal Guidance
Chenyangguang Zhang, Zhiqiang Lou, Yan Di +2
Real-time monocular 3D reconstruction is a challenging problem that remains unsolved. Although recent end-to-end methods have demonstrated promising results, tiny structures and ge…
Learning to Annotate Part Segmentation with Gradient Matching
Yu Yang, Xiaotian Cheng, Hakan Bilen +1
The success of state-of-the-art deep neural networks heavily relies on the presence of large-scale labelled datasets, which are extremely expensive and time-consuming to annotate.…
Multi-Camera Collaborative Depth Prediction via Consistent Structure Estimation
Jialei Xu, Xianming Liu, Yuanchao Bai +4
Depth map estimation from images is an important task in robotic systems. Existing methods can be categorized into two groups including multi-view stereo and monocular depth estima…
Deep Lossy Plus Residual Coding for Lossless and Near-lossless Image Compression
Yuanchao Bai, Xianming Liu, Kai Wang +3
Lossless and near-lossless image compression is of paramount importance to professional users in many technical fields, such as medicine, remote sensing, precision engineering and…
CATRE: Iterative Point Clouds Alignment for Category-level Object Pose Refinement
Xingyu Liu, Gu Wang, Yi Li +1
While category-level 9DoF object pose estimation has emerged recently, previous correspondence-based or direct regression methods are both limited in accuracy due to the huge intra…
PUERT: Probabilistic Under-sampling and Explicable Reconstruction Network for CS-MRI
Jingfen Xie, Jian Zhang, Yongbing Zhang +1
Compressed Sensing MRI (CS-MRI) aims at reconstructing de-aliased images from sub-Nyquist sampling k-space data to accelerate MR Imaging, thus presenting two basic issues, i.e., wh…
Self-Supervised Arbitrary-Scale Point Clouds Upsampling via Implicit Neural Representation
Wenbo Zhao, Xianming Liu, Zhiwei Zhong +4
Point clouds upsampling is a challenging issue to generate dense and uniform point clouds from the given sparse input. Most existing methods either take the end-to-end supervised l…
Shadows can be Dangerous: Stealthy and Effective Physical-world Adversarial Attack by Natural Phenomenon
Yiqi Zhong, Xianming Liu, Deming Zhai +2
Estimating the risk level of adversarial examples is essential for safely deploying machine learning models in the real world. One popular approach for physical-world attacks is to…
Occlusion-Aware Self-Supervised Monocular 6D Object Pose Estimation
Gu Wang, Fabian Manhardt, Xingyu Liu +2
6D object pose estimation is a fundamental yet challenging problem in computer vision. Convolutional Neural Networks (CNNs) have recently proven to be capable of predicting reliabl…
GPV-Pose: Category-level Object Pose Estimation via Geometry-guided Point-wise Voting
Yan Di, Ruida Zhang, Zhiqiang Lou +4
While 6D object pose estimation has recently made a huge leap forward, most methods can still only handle a single or a handful of different objects, which limits their application…
Which Style Makes Me Attractive? Interpretable Control Discovery and Counterfactual Explanation on StyleGAN
Bo Li, Qiulin Wang, Jiquan Pei +2
The semantically disentangled latent subspace in GAN provides rich interpretable controls in image generation. This paper includes two contributions on semantic latent subspace ana…
Towards End-to-End Image Compression and Analysis with Transformers
Yuanchao Bai, Xu Yang, Xianming Liu +4
We propose an end-to-end image compression and analysis model with Transformers, targeting to the cloud-based image classification application. Instead of placing an existing Trans…
Deep Attentional Guided Image Filtering
Zhiwei Zhong, Xianming Liu, Junjun Jiang +2
Guided filter is a fundamental tool in computer vision and computer graphics which aims to transfer structure information from guidance image to target image. Most existing methods…
Deep-3D Microscope: 3D volumetric microscopy of thick scattering samples using a wide-field microscope and machine learning
Bowen Li, Shiyu Tan, Jiuyang Dong +4
Confocal microscopy is the standard approach for obtaining volumetric images of a sample with high axial and lateral resolution, especially when dealing with scattering samples. Un…
Almost Optimal Batch-Regret Tradeoff for Batch Linear Contextual Bandits
Zihan Zhang, Xiangyang Ji, Yuan Zhou
We study the optimal batch-regret tradeoff for batch linear contextual bandits. For any batch number , number of actions , time horizon , and dimension , we provide an…
Weakly-Supervised Monocular Depth Estimationwith Resolution-Mismatched Data
Jialei Xu, Yuanchao Bai, Xianming Liu +2
Depth estimation from a single image is an active research topic in computer vision. The most accurate approaches are based on fully supervised learning models, which rely on a lar…
SO-Pose: Exploiting Self-Occlusion for Direct 6D Pose Estimation
Yan Di, Fabian Manhardt, Gu Wang +3
Directly regressing all 6 degrees-of-freedom (6DoF) for the object pose (e.g. the 3D rotation and translation) in a cluttered environment from a single RGB image is a challenging p…
Learning with Noisy Labels via Sparse Regularization
Xiong Zhou, Xianming Liu, Chenyang Wang +3
Learning with noisy labels is an important and challenging task for training accurate deep neural networks. Some commonly-used loss functions, such as Cross Entropy (CE), suffer fr…
Asymmetric Loss Functions for Learning with Noisy Labels
Xiong Zhou, Xianming Liu, Junjun Jiang +2
Robust loss functions are essential for training deep neural networks with better generalization power in the presence of noisy labels. Symmetric loss functions are confirmed to be…
TransMIL: Transformer based Correlated Multiple Instance Learning for Whole Slide Image Classification
Zhuchen Shao, Hao Bian, Yang Chen +4
Multiple instance learning (MIL) is a powerful tool to solve the weakly supervised classification in whole slide image (WSI) based pathology diagnosis. However, the current MIL met…
Anti-aliasing Semantic Reconstruction for Few-Shot Semantic Segmentation
Binghao Liu, Yao Ding, Jianbin Jiao +2
Encouraging progress in few-shot semantic segmentation has been made by leveraging features learned upon base classes with sufficient training data to represent novel classes with…